1. Field of the Invention
This present invention relates to the field of energy recovery systems and related methods.
2. Description of the Related Art
Many different types of processes consume energy to obtain an output result, or to produce a required product or compound. For example, chemical processes consume energy to provide a desired result. For large scale processes which consume significant amounts of energy, it is preferable to minimize the energy consumed where possible. In the case of, for example, electrical energy generation systems or relatively large manufacturing plant or factories, it is preferable to optimize and potentially minimize the consumption of energy through careful operation, design or reconfiguration of the plant and equipment used.
For example, in some industrial manufacturing processes, specific streams of material flows need to be supplied to different types of equipment and machinery at specific temperatures. These material flows may need to be heated or cooled from an original starting temperature to a target temperature. This in turn will require the consumption of energy to cool specific streams, and also to heat other specific streams.
The total energy employed or consumed by the processes can be optimized to a global minimal level through, for example, careful placement and configuration of specific material streams with respect to one another. For example, there may be the potential for hot streams that require cooling to be placed in proximity with cold streams that require heating. Thermal energy already present in streams that needs to be removed, or streams that need to have heat added, can therefore be associated with one another to optimize the energy consumption of the process. In addition, the minimum temperature differences between hot streams and cold streams upon optimization can also result in huge savings in energy consumption.
These considerations can be taken into account during the energy targeting phase prior to design, or alternatively, during the reconfiguration or refitting of the plant or equipment. It is preferable to consider these optimization issues with a modeling system prior to the actual design, redesign, construction or modification of the actual plant and equipment.
The state-of-the-art software currently on the market includes AspenTech Inc. software known as Aspen Pinch, Hyprotech Inc. software known as HX-NET (acquired by AspenTech), Pinch Express of KBC and Sprint of UMIST. In the targeting phase, these software products allow specific stream conditions of a process to be tracked and individual operational attributes associated with these streams to be modeled and adjusted, if required. In general terms, such software products are normally employed to track the temperatures and heat capacity flows of specific material streams in a process. Although such software provides useful tools, they are not particularly flexible in application, and do not address some of the above problems systematically.
For example, in grassroots heat exchanger network design, the parameter known as global ΔTmin (minimum approach temperature) is typically used in the state-of-the-art commercial software to represent the desired level of heat recovery between hot and cold streams, such as, for example, the minimum temperature difference allowed to recover energy through a heat exchanger. A single value for a specific material stream attribute can only be adjusted at one time including the ΔTmin. This forces a user of the system to employ a trial and error approach through ‘tweaking’ particular attributes of specific streams one at a time, to hopefully arrive at an optimized value for Q, and/or Qh, which represent the total energy consumed for heating (Qh) and the total energy consumed for cooling (Qc) for the process model at optimal driving force distribution between the hot and the cold streams. This limitation becomes compounded and makes the existing software difficult to employ effectively in large-scale processes, which employ many material streams, where these material streams may have a number of operational attributes which can be modeled and adjusted. This is also the case when several sets of stream-specific minimum temperature approaches need to be analyzed for optimal driving force distribution determination in the studied energy system.
Further, process stream changes not only may result in a reduction in energy utility, but also may bring a reduction in ΔTmin. Thus, in current state-of-the-art technology, the capital/energy trade-off in energy system synthesis must be readjusted after each process change. Recognized by the inventors is that the change in the driving force distribution due to both stream-specific ΔTmin and each process change also affects the utility level selection. The problem is interlinked and multi-dimensional in that process changes and stream-specific ΔTmin selection are competing for optimal selection of utilities, optimal process conditions, and optimal energy recovery system synthesis.
Two main methods are currently in use to address such issues: mathematical programming and thermodynamic-heuristics based pinch technology. Both methods fail to solve the problem of finding optimal driving force distribution, systematically, due to both optimal process conditions and optimal stream-specific ΔTmin in energy systems and without manual iteration. Optimal driving force distribution in energy systems comes from the combined effect of the system's process conditions and hot and cold stream minimum approach temperatures ΔTmin. Optimal driving force distribution in energy systems can have a significant impact on energy consumption, utility selection, utility systems, and energy recovery systems capital investment. Therefore, recognized by the inventors is that any proposed method for optimal energy recovery systems design/retrofit and optimization should address these issues systematically and without enumeration.
Currently, there are no methods or program products that can handle the theoretical, practical, and economical energy targeting problems under variable driving force distribution to find optimal distribution without manual iterations (manual data entry-trial and error) and in a user-friendly manner.
NZ Patent No. 527,244 (July 2004) and WO Application No. 2005/010,783 (February 2005) have addressed the problem of energy targeting to find an optimal driving force distribution due to process conditions optimization, but only for a global ΔTmin, and not for stream-specific ΔTmin. Other prior teachings have suggested using heuristics to find the energy utility targets and an optimal driving force distribution at constant process conditions and stream-dependent ΔTmin. Still other prior teachings have tried to find an optimal driving force distribution through process conditions optimization at a fixed global ΔTmin using mathematical programming.
Recognized by the inventors, however, is that in most industrial processes, it is at least inefficient, if not impractical, to require that all heat exchangers (and thus, all process streams and utilities) obey the same global minimum value for driving forces, since streams (and utilities) in general have very different heat transfer coefficients. Quite often, the difference in film heat transfer coefficients can be several orders of magnitude. Thus, some heat exchangers require large ΔTmin values in order to avoid requiring an excessive heat transfer area, while other units will manage well with much smaller ΔTmin values. In addition, the hot stream-specific minimum approach temperature optimal set can lead to much better energy consumption targets.
When considering retrofitting, the same problems exist with still no practical solution to simultaneously finding energy targets under all possible combinations of different process conditions, while using stream-specific minimum approach temperatures (ΔTmini).
Some scientists have recognized the need for at least assigning individual contributions to the minimum driving forces for each stream and utility, based upon the heat transfer coefficient of matched streams. These ΔTmin contributions may not only reflect heat transfer conditions, but may be used to represent the need for expensive materials of construction, heat exchanger types, etc. These methods, however, are based upon heuristics and are iterative, lack systemization, and do not consider the possible changes in process conditions that can result in significant changes in the energy system driving force distribution.
Recognized by the inventors is that it would be beneficial to have a system, method and program product that utilizes both process conditions manipulation and stream-specific minimum approach temperatures ΔTmini to target for energy consumption, utility selection and design heat recovery systems at an optimal driving force distribution systematically, without manual iteration or enumeration, without customized modeling, and in a user friendly manner.